




'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor

class 	_snake_case(			unittest.TestCase      ):






 def __init__(self       :  str    ,		a       :  Optional[Any]    ,		a       :  Optional[int]=7    ,		a       :  List[str]=3    ,		a       :  Tuple=18    ,		a       :  List[str]=30    ,		a       :  Dict=4_00    ,		a       :  Dict=True    ,		a       :  Dict=None    ,		a       :  Any=True    ,		a       :  Any=False    ,		a       :  List[str]=True    ,		a       :  Union[str, Any]=True    ,		a       :  Optional[int]=[0.5, 0.5, 0.5]    ,		a       :  Tuple=[0.5, 0.5, 0.5]    ,		)       ->   Dict:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     num_channels
    A__       =     image_size
    A__       =     min_resolution
    A__       =     max_resolution
    A__       =     do_resize
    A__       =     size if size is not None else {'height': 18, 'width': 20}
    A__       =     do_thumbnail
    A__       =     do_align_axis
    A__       =     do_pad
    A__       =     do_normalize
    A__       =     image_mean
    A__       =     image_std






 def 						_UpperCamelCase (self       :  str    )       ->   Dict:






    """simple docstring"""


    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_thumbnail": self.do_thumbnail,
        "do_align_long_axis": self.do_align_axis,
        "do_pad": self.do_pad,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }




@require_torch
@require_vision
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Union[str, Any]						=					DonutImageProcessor if is_vision_available() else None






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     DonutImageProcessingTester(self    )






 @property
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()






 def 						_UpperCamelCase (self       :  Tuple    )       ->   List[Any]:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    self.assertTrue(hasattr(a    ,		'do_resize'    )    )
    self.assertTrue(hasattr(a    ,		'size'    )    )
    self.assertTrue(hasattr(a    ,		'do_thumbnail'    )    )
    self.assertTrue(hasattr(a    ,		'do_align_long_axis'    )    )
    self.assertTrue(hasattr(a    ,		'do_pad'    )    )
    self.assertTrue(hasattr(a    ,		'do_normalize'    )    )
    self.assertTrue(hasattr(a    ,		'image_mean'    )    )
    self.assertTrue(hasattr(a    ,		'image_std'    )    )






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   str:






    """simple docstring"""


    A__       =     self.image_processing_class.from_dict(self.image_processor_dict    )
    self.assertEqual(image_processor.size    ,		{'height': 18, 'width': 20}    )

    A__       =     self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42    )
    self.assertEqual(image_processor.size    ,		{'height': 42, 'width': 42}    )

    # Previous config had dimensions in (width, height) order
    A__       =     self.image_processing_class.from_dict(self.image_processor_dict    ,		size=(42, 84)    )
    self.assertEqual(image_processor.size    ,		{'height': 84, 'width': 42}    )






 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[Any]:






    """simple docstring"""


    pass






 @is_flaky()
 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random PIL images
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		Image.Image    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,		)






 @is_flaky()
 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   int:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random numpy tensors
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    ,		numpify=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		np.ndarray    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,		)






 @is_flaky()
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random PyTorch tensors
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    ,		torchify=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		torch.Tensor    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,		)








'''simple docstring'''







import socket







def 						_A      ( ):






   '''simple docstring'''







   A__       =     socket.socket(socket.AF_INET      ,socket.SOCK_STREAM						)
   A__       =     socket.gethostname()
   A__       =     12312

   sock.connect((host, port)						)
   sock.send(B'Hello server!'						)

   with open('Received_file'      ,'wb'						) as out_file:
      print('File opened'						)
      print('Receiving data...'						)
      while True:
         A__       =     sock.recv(1024						)
         if not data:
            break
         out_file.write(UpperCAmelCase						)

   print('Successfully received the file'						)
   sock.close()
   print('Connection closed'						)


if __name__ == "__main__":
    main()


